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With increasing diversity in Deep Neural Network(DNN) models in terms of layer shapes and sizes, the research community has been investigating flexible/reconfigurable accelerator substrates.
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2018
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2018
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2018
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2019
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2019
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2019
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Z. Zhao, H. Kwon, S. Kuhar, W. Sheng, Z. Mao, and T. Krishna, “mrna: Enabling efficient mapping space exploration for a reconfiguration neural accelerator,” in 2019 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) . IEEE, 2019, pp. 282–292
2019
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2020
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2020
Later among the works it cites.
S.-C. Kao, G. Jeong, and T. Krishna, “Confuciux: Autonomous hardware resource assignment for dnn accelerators using reinforcement learning,” in 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO) . IEEE, 2020, pp. 622–636
2020
Later among the works it cites.
S.-C. Kao and T. Krishna, “Gamma: Automating the hw mapping of dnn models on accelerators via genetic algorithm,” in ICCAD , 2020
2020
Later among the works it cites.
E. Qin, A. Samajdar, H. Kwon, V. Nadella, S. Srinivasan, D. Das, B. Kaul, and T. Krishna, “Sigma: A sparse and irregular gemm accelerator with flexible interconnects for dnn training,” in 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA) , 2020
2020
Later among the works it cites.
A. Samajdar, J. M. Joseph, Y. Zhu, P. Whatmough, M. Mattina, and T. Krishna, “A systematic methodology for characterizing scalability of dnn accelerators using scale-sim,” in 2020 IEEE International Symposium on Performance Analysis of Systems and Software(ISPASS) , 2020
2020
Later among the works it cites.
K. Hegde, P.-A. Tsai, S. Huang, V. Chandra, A. Parashar, and C. W. Fletcher, “Mind mappings: enabling efficient algorithm-accelerator mapping space search,” in Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems , 2021, pp. 943–958
2021
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